Mobile customers are increasingly demanding service availability, continuity, and consistency. They expect to have access to a wide variety of services all the time and they expect their services to be stable and reliable. Such services do not only include standard telephony services, but further include multimedia telephony, Internet, mobile Television (mobile TV) and other media services. Further, mobile customers expect to receive these services at an increasingly high service quality.
At the same time, wireless operators must optimize their network and run their operations efficiently and seamlessly to deliver and maintain these services over complex multi-vendor and multi-technology networks that consist of legacy and Next Generation (NGN) wireless technologies. Service providers must focus on proactively improving their customers' service quality and availability and optimizing network performance to meet increasing customer retention.
Therefore, the monitoring and evaluation of network performance, in particular the observation of service performance as seen by the customer are becoming increasingly important for network operators in the management and operation of communication networks. Typically measures like throughput, delay, loss, jitter and further service quality metrics derived from these basic performance descriptors are investigated and evaluated in service centric management for a specific communication service.
Examples for service quality metrics for different types of communication services are end-to-end throughput as performance metrics for the generic mobile broadband service, and mobile TV Quality of Experience (QoE) as standardized in 3GPP for the mobile TV service.
There are basically two main sources of information to collect performance data from. The performance data can either be collected from the network or from the mobile terminals, e.g. User Equipments (UEs) in terms of the Universal Mobile Telecommunications System (UMTS) in the network. The performance data collected from the network may include radio link performance for the UE, cell status information, transport network status information or the like. Regarding the second source of information, i.e. the UE, it is possible to collect information both about the radio network (such as radio link quality) but also about application perceived quality. However, collecting service quality reports from the UE in the network is difficult or impossible in most typical cases, as the application servers are typically outside of the domain of the cellular network operator and, hence, the application performance measurements can not be accessed.
Current cellular systems including 2G/3G systems and the newly emerging Long Term Evolution (LTE) system support the trace functionality to collect information from the network for a particular user session. Further, these systems support Performance Management (PM) counters and Key Performance Indicators (KPIs) providing aggregate cell or network level information. During tracing all network activities related to a particular user can be logged and later delivered to a central management entity for evaluation. Logged network activities include signaling messages sent/received either on the radio interface or network node interfaces.
Subscriber and equipment trace provide very detailed information at call level on one or more specific mobile terminals. Contrary to performance measurements, which are a permanent source of information, trace is activated on user demand for a limited period of time for specific analysis purposes. Trace plays a major role in activities such as determination of the root cause of a malfunctioning mobile, advanced troubleshooting, optimization of resource usage and quality, Radio Frequency (RF) coverage control and capacity improvement, dropped call analysis, Core Network and UMTS Terrestrial Radio Access Network (UTRAN) end-to-end UMTS procedure validation and so on.
The capability to log data on any interface at call level for a specific user (e.g. identified by the International Mobile Subscriber Identity (IMSI)) or mobile type (e.g. identified by the International Mobile Station Equipment Identity (IMEI) or International Mobile Station Equipment Identity Software Version (IMEISV)), or service initiated by a user allows getting information which cannot be deduced from performance measurements such as perception of end-user Quality of Service (QoS) during his call (e.g. requested QoS vs. provided QoS), correlation between protocol messages and RF measurements, or interoperability with specific mobile vendors. Moreover, performance measurements provide values aggregated on an observation period, whereas subscriber and mobile terminal trace give instantaneous values for a specific event (e.g. call, location update, etc.). In order to produce this data, subscriber and mobile terminal trace are carried out in the Network Elements (NEs) of the network. The data can then be transferred to an external system.
The cell traffic trace functionality, also available in 3GPP systems allows logging and collecting activities of all users located in particular cell(s). Thereby, the selection of users for tracing can be done based on cells rather than particular IMSI or IMEI as it was the case in subscriber and equipment trace. In this way the subscriber and equipment trace together with cell traffic trace complement each other and provide a large degree of freedom for the operator to collect measurements and logging data from the network.
More recently 3GPP has started to work on the concept of Minimization of Drive Test (MDT) measurements, which enables to instruct UEs to perform certain radio measurements and to collect them in the management system with the purpose to replace some of the drive tests of the operators with measurements done by customer devices. Fur this purpose, an equipment (test mobile) that collects measurements and location information collects all the required information and this information is used offline to analyze the coverage in different locations. Based thereon, the parameters, power, antenna locations, antenna tilts, etc. are optimized. After the changes to any of the optimization parameters, the drive test has to be undertaken again to make sure that the impact of these changes is positive. Using drive tests for network optimization purposes is costly and burdensome. Thus, it is desirable to develop automated solutions reduce the operator costs for network deployment and operation.
Further, so far, MDT measurements currently include only measurements performed on the radio link.
For collection of UE service and application layer performance statistics there are standards which define service quality reports from the terminal, also called Quality of Experience (QoE) reports. The service quality or QoE reports are typically defined individually for a given application/service as the parameters used to describe the QoS largely depend on the type and characteristics of a service. Such QoE reports are defined for instance, for Mobile TV, Internet Protocol Television (IPTV), multimedia telephony services and the definition of such reporting functions are often spread across several standardization bodies, such as 3GPP, TISPAN, BBF, Open IPTV Forum, ITU-T.
In addition to classifying performance measurements according to whether the terminal or the network performs the measurement, an orthogonal dimension to classify measurement methods is to differentiate between active and passive measurements. In case of passive measurements the measurements are done on the live user traffic without impacting the ongoing traffic in any way. In case of active measurements specific test traffic is generated and the measurements are performed on that generated test traffic. The drawback of active measurements is that they impact the ongoing regular traffic and thereby influence the measurement environment itself. The benefit, however, is that it gives control over the measurement traffic and thereby it enables to choose the characteristics of the traffic, as well as, the time instant and the network parts when and where the measurements are to be performed.
In today's networks there exists an entity called Network Management System (NMS) that is responsible for the management and operation of the network and for the observability of the quality of its offered services. The operation of NMS is based on continuous live network information that is typically coming from logs and measurements.
Regardless of whether the measurements going into the NMS are passive or active, and regardless of whether these measurements are performed by the terminal (e.g. by QoE reporting) or done by the network, the main principle behind the NMS is to collect significant amount of measurements (also called “samples”) from the usage of a service and then to process them and provide a reliable view of the performance of the particular service. The individual service usage transactions, e.g., in a mobile data network, have many—in most cases independent—dimensions such as, location (cell), perceived radio quality, type and capability of UE, background load in the cell, time of the day and so on, which of course all have effect on the service performance.
One important task of the NMS is to pinpoint the main reasons if the service performance differs from the expectations in order to provide fault localization and root cause analysis functionality for the network operator. To achieve this, the sample measurements are statistically aggregated by specialized algorithms and analyzed along the specific dimensions, cross correlations are filtered, and reasoning is given. A key issue for such NMS functionality to work properly is to have enough sample measurements across all the above mentioned measurement dimensions like location, radio quality, UE capability, network load, time, etc. It can happen that there are certain time periods, cells, UEs or combinations of these, in which circumstances there are not enough measurements for a given service. However, a sufficient amount of samples across all dimensions is needed in order to provide meaningful network performance analysis.
Additional sample measurement generation through drive tests is, however, typically expensive and often requires a huge arsenal of equipment, mobility, and time to execute. Therefore, other means for ensuring the sufficient number of sample measurements entering into the NMS are necessary.